Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. SE(3)-transformers: 3D roto-translation equivariant attention networks. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 1970–1981, 2020.
William Fulton and Joe Harris. Representation theory: a first course, volume 129. Springer Science & Business Media, 2013.
Simon Geisler, Yujia Li, Daniel Mankowitz, Ali Taylan Cemgil, Stephan Gunnemann, and Cosmin Paduraru. Transformers meet directed graphs. preprint arXiv:2302.00049, 2023.
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In International Conference on Machine Learning (ICML), pages 1263-1272. PMLR, 2017.
Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008.
Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, and Xavier Bresson. A generalization of ViT/MLP-Mixer to graphs. preprint arXiv:2212.13350, 2022.
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 22118-22133, 2020.
Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, and Pan Li. On the stability of expressive positional encodings for graph neural networks. arXiv preprint arXiv:2310.02579, 2023.
Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian. Global self-attention as a replacement for graph convolution. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 655–665, 2022.
Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, and Siamak Ravanbakhsh. Equivalence with learned canonicalization functions. In International Conference on Machine Learning (ICML), pages 15546-15566. PMLR, 2023.
Nicolas Keriven and Samuel Vaiter. What functions can graph neural networks compute on random graphs? the role of positional encoding. arXiv preprint arXiv:2305.14814, 2023.
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. Pure transformers are powerful graph learners. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015.
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel. Neural relational inference for interacting systems. In International Conference on Machine Learning (ICML), pages 2688-2697. PMLR, 2018.
Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR), volume 5, 2017.
Jonas Kohler, Leon Klein, and Frank Noé. Equivariant flows: exact likelihood generative learning for symmetric densities. In International Conference on Machine Learning (ICML), pages 5361-5370. PMLR, 2020.
Risi Kondor and Shubhendu Trivedi. On the generalization of equivariance and convolution in neural networks to the action of compact groups. In International Conference on Machine Learning, pages 2747-2755. PMLR, 2018.
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou. Rethinking graph transformers with spectral attention. In Advances in Neural Information Processing Systems (NeurIPS), volume 34, 2021.